The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks

article The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks published yes Dmitrii Zendrikov author Alexander Paraskevov author d05e3c56-9262-11ed-9231-be692464e5ac TiVo department Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning. project Thin pancake-like neuronal networks cultured on top of a planar microelectrode array have been extensively tried out in neuroengineering, as a substrate for the mobile robot’s control unit, i.e., as a cyborg’s brain. Most of these attempts failed due to intricate self-organizing dynamics in the neuronal systems. In particular, the networks may exhibit an emergent spatial map of steady nucleation sites (“n-sites”) of spontaneous population spikes. Being unpredictable and independent of the surface electrode locations, the n-sites drastically change local ability of the network to generate spikes. Here, using a spiking neuronal network model with generative spatially-embedded connectome, we systematically show in simulations that the number, location, and relative activity of spontaneously formed n-sites (“the vitals”) crucially depend on the samplings of three distributions: (1) the network distribution of neuronal excitability, (2) the distribution of connections between neurons of the network, and (3) the distribution of maximal amplitudes of a single synaptic current pulse. Moreover, blocking the dynamics of a small fraction (about 4%) of non-pacemaker neurons having the highest excitability was enough to completely suppress the occurrence of population spikes and their n-sites. This key result is explained theoretically. Remarkably, the n-sites occur taking into account only short-term synaptic plasticity, i.e., without a Hebbian-type plasticity. As the spiking network model used in this study is strictly deterministic, all simulation results can be accurately reproduced. The model, which has already demonstrated a very high richness-to-complexity ratio, can also be directly extended into the three-dimensional case, e.g., for targeting peculiarities of spiking dynamics in cerebral (or brain) organoids. We recommend the model as an excellent illustrative tool for teaching network-level computational neuroscience, complementing a few benchmark models. https://research-explorer.ista.ac.at/download/17886/18825/2024_NeuralNetworks_Zendrikov.pdf application/pdfno Elsevier2024 eng Neural Networks 0893-6080 1879-2782 39217864 00131647460000110.1016/j.neunet.2024.106589 180 Zendrikov, Dmitrii, and Alexander Paraskevov. “The Vitals for Steady Nucleation Maps of Spontaneous Spiking Coherence in Autonomous Two-Dimensional Neuronal Networks.” Neural Networks. Elsevier, 2024. <a href="https://doi.org/10.1016/j.neunet.2024.106589">https://doi.org/10.1016/j.neunet.2024.106589</a>. Zendrikov D, Paraskevov A. The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks. Neural Networks. 2024;180. doi:<a href="https://doi.org/10.1016/j.neunet.2024.106589">10.1016/j.neunet.2024.106589</a> D. Zendrikov and A. Paraskevov, “The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks,” Neural Networks, vol. 180. Elsevier, 2024. Zendrikov, D., & Paraskevov, A. (2024). The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks. Neural Networks. Elsevier. <a href="https://doi.org/10.1016/j.neunet.2024.106589">https://doi.org/10.1016/j.neunet.2024.106589</a> Zendrikov D, Paraskevov A. 2024. The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks. Neural Networks. 180, 106589. Zendrikov, Dmitrii, and Alexander Paraskevov. “The Vitals for Steady Nucleation Maps of Spontaneous Spiking Coherence in Autonomous Two-Dimensional Neuronal Networks.” Neural Networks, vol. 180, 106589, Elsevier, 2024, doi:<a href="https://doi.org/10.1016/j.neunet.2024.106589">10.1016/j.neunet.2024.106589</a>. D. Zendrikov, A. Paraskevov, Neural Networks 180 (2024). 178862024-09-08T22:01:10Z2025-09-08T09:12:20Z